Thermal comfort and energy performance of public rental housing under typical and near-extreme weather conditions in Hong Kong

© 2017 Elsevier B.V. Building performance evaluation is crucial for sustainable urban developments. In high-density cities, occupants suffer from poor living conditions due to building overheating, especially during increasingly frequent near-extreme summer conditions caused by climate change. To represent this situation, the summer reference year weather data was employed for building simulations using DesignBuilder. This study aims to evaluate the thermal comfort and energy consumption of four typical public rental housing (PRH) building types in Hong Kong. For free-running flats, results show generally higher air temperatures in the oldest PRH type (Slab) with a compact linear building form and the most sensitive response to outdoor temperature changes for another older PRH type (Trident) with a Y-shaped design, possibly owing to its high wall conductivity. Occupants in all building types experience a ???10% increase in the proportion of discomfort hours when compared to results for typical summer conditions, but overheating is the most severe in Slab type PRH. Following an initial assessment of the cooling energy usage, a simple sensitivity test was conducted to explore the potential energy savings by various passive design strategies, including shading and reducing the exposed cooled space. A cross-shaped building form also appears to be more energy efficient. These findings, complemented by further parametric analyses, may prove useful when designing buildings for climate change

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Current and emerging developments in subseasonal to decadal prediction

Weather and climate variations of subseasonal to decadal timescales can have enormous social, economic and environmental impacts, making skillful predictions on these timescales a valuable tool for decision makers. As such, there is a growing interest in the scientific, operational and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) timescales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) timescales, while the focus remains broadly similar (e.g., on precipitation, surface and upper ocean temperatures and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal and externally-forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correct, calibration and forecast quality assessment; model resolution; atmosphere-ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Prograame (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

On The Seasonal Predictability of East Asian Rainfall and Rapidly Intensifying North Atlantic Tropical Cyclones

The provision of accurate weather and climate predictions at timescales of days to decades has been a major research goal in atmospheric and oceanic sciences. This dissertation explores the issue of 'seasonal predictability', the potential of the climate system being predicted a season in advance. The studies in this dissertation examine the seasonal predictability of two aspects of the climate system: (1) rainfall, including its extremes; and (2) tropical cyclones (TCs), particularly those that undergo rapid intensification (RI). The first study examines the response of rainfall in East Asia to the El Nino-Southern Oscillation (ENSO) phenomenon, and demonstrates an asymmetric response of rainfall to ENSO along the southeastern coast of China during boreal fall/winter. Anomalous rainfall is observed during both El Nino and La Nina compared to the ENSO-Neutral phase. We argue that precipitation anomalies during El Nino arise from anomalous onshore moisture fluxes, while those during La Nina are driven by the persistence of terrestrial moisture anomalies from earlier excess rainfall in this region, highlighting the role of land-atmosphere interactions in maintaining ENSO-climate teleconnections. The second study explores the observational connections between the large-scale environment and the seasonal statistics of rapidly intensifying North Atlantic TCs. For TCs in the Central/Eastern tropical North Atlantic, the interannual variability of their probability to experience RI is influenced by the seasonal large-scale environment, but not for TCs over the Gulf of Mexico and Western Caribbean Seas. We suggest that this differentiated response is due to the former region exhibiting negatively correlated seasonal anomalies of vertical wind shear and potential intensity. This motivates a subsequent chapter, which examines the physical mechanisms behind the negative correlation, and applies the findings to global TC basins. The final chapter extends the environmental controls on RI to numerical models, and explores: (1) the simulation of the seasonal large-scale environment in climate models, as an indirect means of RI seasonal predictability; (2) the role of large-scale environmental biases in TC intensity biases in weather forecast models. Assessment of RI predictability through weather and climate models will contribute to the long-term research effort in TC modeling and prediction

Large-scale environmental controls on the seasonal statistics of rapidly intensifying North Atlantic tropical cyclones

This study is concerned with the connections between the large-scale environment and the seasonal occurrence of rapid intensification (RI) of North Atlantic tropical cyclones. Physically-motivated statistical analysis using observations and reanalysis products suggests that for tropical cyclones over the open tropical North Atlantic, the interannual variability of the probability of storms undergoing RI is influenced by seasonal large-scale atmospheric and oceanic variables, but not so for storms over the Gulf of Mexico and western Caribbean Sea. We suggest that this differentiated response is due to the former region exhibiting a strong negative correlation between the seasonal anomalies of vertical wind shear and potential intensity. Differences in the mean climatology and subseasonal variations of the large-scale environment in these regions appear to play an insignificant role in the distinctive seasonal environmental controls on RI. We suggest that the interannual correlation of vertical wind shear and potential intensity is an indicator of seasonal predictability of tropical cyclone activity (including RI) across the tropics

DANP-anchored hairpin primer sequences used for detection of CHIKV.

<p>DANP-anchored hairpin primer sequences used for detection of CHIKV.</p

Optimization of number of PCR cycles.

<p>DANP-anchored RT-PCR is carried out with and without the presence of CHIKV RNA template and the fluorescence intensity is measured after every 5 PCR cycles from both before and after PCR reactions. The fluorescence intensity starts to increase significantly after 20 cycles when CHIKV RNA is present and reaches saturation after 30 cycles, while that of NTC also starts to increase slowly from 25 to 30 cycles and become more obvious afterwards. As a result, the maximum difference in fluorescence intensity can be achieved after 30 cycles of PCR reaction. Data are shown as means SEM of five experiments. ***P < 0.001, **P < 0.01 by multiple t-test.</p